Development and expert inspections of the user interface for a primary care decision support system.

Background: General practitioners play a unique key role in diagnosing patients with unclear diseases. Decision support systems in primary care can assist with diagnosis provided that they are efficient and user-friendly.

Objectives: The objective of this study is to develop a high-fidelity prototype of the user interface of a clinical decision support system for primary care, particularly for diagnosis support in unclear diseases, using expert inspections at an early stage of development to ensure a high level of usability.

How to customize common data models for rare diseases: an OMOP-based implementation and lessons learned.

Background: Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases.

Secondary use of patient data within decentralized studies using the example of rare diseases in Germany: A data scientist's exploration of process and lessons learned.

Objective: Unlocking the potential of routine medical data for clinical research requires the analysis of data from multiple healthcare institutions. However, according to German data protection regulations, data can often not leave the individual institutions and decentralized approaches are needed. Decentralized studies face challenges regarding coordination, technical infrastructure, interoperability and regulatory compliance.

Protocol and preregistration for the CODEC project: measuring, modelling and mechanistically understanding the nature of cognitive variability in early childhood.

Background: Children's cognitive performance fluctuates across multiple timescales. However, fluctuations have often been neglected in favour of research into average cognitive performance, limiting the unique insights into cognitive abilities and development that cognitive variability may afford. Preliminary evidence suggests that greater variability is associated with increased symptoms of neurodevelopmental disorders, and differences in behavioural and neural functioning.

Web-Based, Human-Guided, or Computer-Guided Transdiagnostic Cognitive Behavioral Therapy in University Students With Anxiety and Depression: Randomized Controlled Trial.

Background: Internet-based cognitive behavioral interventions (iCBTs) are efficacious treatments for depression and anxiety. However, it is unknown whether adding human guidance is feasible and beneficial within a large educational setting.

Objective: This study aims to potentially demonstrate the superiority of 2 variants of a transdiagnostic iCBT program (human-guided and computer-guided iCBT) over care as usual (CAU) in a large sample of university students and the superiority of human-guided iCBT over computer-guided iCBT.